ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

tests/test_ch3_english_figures.py

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+import re
+from pathlib import Path
+from xml.etree import ElementTree
+
+ROOT = Path(__file__).resolve().parents[1]
+CHAPTER = ROOT / "book-en" / "chapter3.md"
+IMAGE_DIR = ROOT / "book-en" / "images"
+
+EXPECTED_ANCHORS = {
+    1: ("User Memory (Individual Scale)", "Knowledge Base (Group Scale)"),
+    2: ("Simple Notes", "Advanced JSON Cards"),
+    3: ("v2 (2025 paper)", "v3 (April 2026)"),
+    4: ("Working Memory", "Procedural"),
+    5: ("① User Query", "④ Generate"),
+    6: ("Word2Vec", "BGE-M3"),
+    7: ("Layer 2 (sparse · long-range connections)", "O(log N) query complexity"),
+    8: ("Term frequency saturation (TF)", "Length normalization (b)"),
+    9: ("Dense retrieval", "Sparse retrieval (BM25)", "Neural\nRe-ranking"),
+    10: ("Global Summary", "Bottom-up Recursive Abstraction"),
+    11: ("My Dentist", "Multi-hop reasoning"),
+    12: ("Non-agentic RAG", "Agentic RAG"),
+    13: ("Agent (ReAct Loop)", "Knowledge Base Backend (Switchable)"),
+    14: ("Traditional chunking (no context)", "Context-aware chunking"),
+    15: ("Phase 1: Knowledge Extraction and Structuring", "Phase 2: Factor Analysis and Knowledge Modeling"),
+}
+
+
+def svg_text(path: Path) -> str:
+    root = ElementTree.parse(path).getroot()
+    return "\n".join(text.strip() for text in root.itertext() if text.strip())
+
+
+def test_chapter_3_references_each_numbered_figure_once():
+    markdown = CHAPTER.read_text(encoding="utf-8")
+    references = [
+        int(number)
+        for number in re.findall(r"images/fig3-(\d+)\.svg", markdown)
+    ]
+
+    assert references == list(range(1, 16))
+
+
+def test_chapter_3_english_figures_match_their_captions():
+    for number, anchors in EXPECTED_ANCHORS.items():
+        text = svg_text(IMAGE_DIR / f"fig3-{number}.svg")
+        for anchor in anchors:
+            assert anchor in text, f"Figure 3-{number} is missing {anchor!r}"